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Traffic Control Strategies for Congested Heterogeneous Multi-Vehicle Networks

University of North Carolina Charlotte-Pouria Karimi Shahri, Amir H. Ghasemi, Vahid Izadi
  • Technical Paper
  • 2020-01-0086
To be published on 2020-04-14 by SAE International in United States
The primary goal of this paper is to pioneer and develop robust and adaptive algorithms for controlling autonomous vehicles in heterogeneous networks with the aim of maximizing the performance (in terms of mobility) and minimizing variation in the network. While the fundamental approaches and models proposed in this research can be applied to any heterogeneous multi-agent system, we select heterogeneous traffic networks as a set-up for exploring the proposed research. We consider the heterogeneity in the system in the form of a mix of autonomous and human-driven vehicles (different levels of autonomous vehicle penetration). We propose a two-level hierarchical controller wherein the upper-level controller, an optimization problem using the concept of macroscopic fundamental diagram is formulated to deal with the traffic demand balance problem. At the lower level, using the microscopic models of the network, the control actions for each vehicle will be determined such that he optimal flow received from the upper-level controllers can be tracked.
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Platooning Vehicles Control for Balancing Coupling Maintenance and Trajectory Tracking

Kubota-Ayumi Suzuki
The University of Tokyo-Rui Fukui, Qiwei Ye, Shin’ichi Warisawa
  • Technical Paper
  • 2020-01-0128
To be published on 2020-04-14 by SAE International in United States
Recently, car-sharing services using ultra-compact mobilities have been attracting attention as a means of transportation for one or two passengers in urban areas. A platooning system consisting of a manned leader vehicle and unmanned follower vehicles can reduce vehicle distributors. We have proposed a platooning system which controls vehicle motion based on the relative position and posture measured by non-contact coupling devices installed between vehicles. The feasibility of the coupling devices was validated through a HILS experiment. There are two basic requirements for realizing our platooning system; (1) all devices must remain coupled and (2) follower vehicles must be able to track the leader vehicle trajectory. Thus, this paper proposes two vehicle control method for satisfying those requirements. They are the “device coupling and trajectory tracking merging method” and the “trajectory shifting method”. The device coupling and trajectory tracking merging method consisting of a coupling keeping controller and a trajectory tracking controller. The predominant controller is chosen according to the amount of the coupling device error and the trajectory tracking error. The trajectory shifting method…
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Real-Time Optimization of Control Strategy for a Range-Extended Electric Vehicle using Reinforcement Learning Algorithm

Nipun Mittal, Bharadwaj Acharya, Chris Paredis, Shubham Bhide, Aditya Pundlikrao Bhagat, Bin Xu
  • Technical Paper
  • 2020-01-1190
To be published on 2020-04-14 by SAE International in United States
Range-Extended Electric Vehicles (REEV) have seen an increase in market share in the past decade. This trend can be attributed to an increased market shift towards electrified powertrains while addressing the range anxiety usually associated with an electric vehicle. In such a scenario, operating the vehicle efficiently is critical to meet the CAFÉ standards. This energy optimization problem becomes even more critical if the vehicle is being operated as part of a fleet as minimal energy savings get compounded across the fleet and result in significant savings for the service provider and more affordability for the customers. There is also an upward trend in ride sharing services operated by fleet owners like Uber and Waymo. Fleet vehicles offer the unique advantage of availability of large amounts of data about the consumer usage pattern in a given area. When coupled with traffic density and immediate destination of the current consumer of the vehicle, the data can assist the improvement of fuel economy while a traditional rule-based strategy can hardly take advantage of the data. Deep Orange…
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RouteE: A Vehicle Energy Consumption Prediction Engine

National Renewable Energy Laboratory-Jacob Holden, Nicholas Reinicke, Jeff Cappellucci
  • Technical Paper
  • 2020-01-0939
To be published on 2020-04-14 by SAE International in United States
The emergence of Connected and Automated Vehicles and Smart Cities technologies create the opportunity for new mobility mode and routing decision tools, among many others. In order to achieve maximal mobility and minimal energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy Prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data is unavailable. Applications include vehicle route selection, energy accounting/optimization in transportation simulation, and corridor energy analyses, among others. The software is an open-source Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own datasets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are trained using NREL’s Future Automotive Systems Technology Simulator (FASTSim) paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center (TSDC) resulting…
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Understanding Charging Flexibility of Shared Autonomous Electric Vehicle Fleets

National Renewable Energy Laboratory-Matthew Moniot, Yanbo Ge, Nicholas Reinicke, Alex Schroeder
  • Technical Paper
  • 2020-01-0941
To be published on 2020-04-14 by SAE International in United States
The combined anticipated trends of vehicle sharing, autonomous control, and powertrain electrification are poised to disrupt the current paradigm of predominately gasoline vehicles with low levels of utilization. Shared, autonomous, electric vehicle (SAEV) fleets, which encompass all three of these trends, have garnered significant interest among the research community due to the opportunity for low-cost mobility with congestion and emissions reductions. This paper explores the charging loads demanded by SAEV fleets in response to servicing personal light-duty vehicle travel demand in four major United States metropolitan areas: Detroit, Austin, Washington DC, and Miami. A coordinated charging model is introduced which minimizes fleet charging costs and corresponding plant emissions in response to different renewable energy penetration rates and shares of personal trip demand served (between 1% and 25%). The relationship between trip demand by time of day, electricity price by time of day, and SAEV fleet size versus overall charging flexibility is explored for each city. SAEV results are presented across various scenarios assuming fleetwide attempts to minimize charging costs while still constrained by offering adequate…
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A Decision Based Mobility Model for Semi and Fully Autonomous Vehicles

Oakland University-Vijitashwa Pandey, Christopher Slon, Line Deschenes
US Army CCDC GVSC-Paramsothy Jayakumar
  • Technical Paper
  • 2020-01-0747
To be published on 2020-04-14 by SAE International in United States
With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs. An example of this is the collection of soil properties data using techniques such as remote sensing. Operators of these vehicles are interested in finding the value of collecting such information.…
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Design and Analysis of Kettering University’s New Proving Ground, the GM Mobility Research Center

Kettering University-Jennifer M. Bastiaan, Craig J. Hoff, Randall S. Beikmann, Scott LaForest
  • Technical Paper
  • 2020-01-0213
To be published on 2020-04-14 by SAE International in United States
Rapid changes in the automotive industry, including the growth of advanced vehicle controls and autonomy, are driving the need for more dedicated proving ground spaces where these systems can be developed safely. To address this need, Kettering University has created the GM Mobility Research Center, a 21-acre proving ground located in Flint, Michigan at the former “Chevy in the Hole” factory location. Construction of a proving ground on this site represents a beneficial redevelopment of an industrial brownfield, as well as a significant expansion of the test facilities available at the campus of Kettering University. Test facilities on the site include a road course and a test pad, along with a building that has garage space, a conference room, and an indoor observation platform. All of these facilities are available to the students and faculty of Kettering University, along with their industrial partners, for the purpose of engaging in advanced transportation research and education. This work describes the history of the proving ground development and outlines its design. Special emphasis is placed on a detailed…
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A Study of Using a Reinforcement Learning Method to Improve Fuel Consumption of a Connected Vehicle with Signal Phase and Timing Data

The University of Alabama-Ashley Phan, Hwan-Sik Yoon
  • Technical Paper
  • 2020-01-0888
To be published on 2020-04-14 by SAE International in United States
Connected and automated vehicles (CAVs) promise to reshape two areas of the mobility industry: the transportation and driving experience. The connected feature of the vehicle uses communication protocols to provide awareness of the surrounding world while the automated feature uses technology to minimize driver dependency. Constituting a subset of connected technologies, vehicle-to-infrastructure (V2I) technologies provide vehicles with real-time traffic light information, or Signal Phase and Timing (SPaT) data. In this paper, the vehicle and SPaT data are combined with a reinforcement learning (RL) method as an effort to minimize the vehicle’s energy consumption. Specifically, this paper explores the implementation of the deep deterministic policy gradient (DDPG) algorithm. As an off-policy approach, DDPG utilizes the maximum Q-value for the state regardless of the previous action performed. In this research, the SPaT data collected from dedicated short-range communication (DSRC) hardware installed at 16 real traffic lights is utilized in a simulated road modeled after a road in Tuscaloosa, Alabama. The vehicle is trained using DDPG and the SPaT data to determine the optimal action to take in…
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Research on Tracking Algorithm for Forward Target Vehicle Using Millimeter-Wave Radar

Beijing University of Posts and Telecomm-yh l
Jilin Univ-Rui He
  • Technical Paper
  • 2020-01-0702
To be published on 2020-04-14 by SAE International in United States
In order to solve the problem that the millimeter-wave radar can’t be directly used for target tracking due to measurement can’t reflect the historical state information of the target, the target measurement information outside the millimeter- wave radar detection range is eliminated by the data plausibility judgment method based on the millimeter-wave radar detection parameters. In order to eliminate clutter interference, target clustering by the Manhattan distance achieve multiple target measurements into one target measurement value. The data association by Nearest Neighbor to determine the measurement information received by the sensor and the real target correspondence. The relative radial distance, relative radial velocity and azimuth angle of the target vehicle detected by the millimeter-wave radar are based on the millimeter-wave radar coordinate system, because the millimeter-wave radar is installed in the front of the vehicle and fixed on the vehicle body. As the vehicle detected by the millimeter-wave radar in the course of driving generally has no vertical direction or vertical velocity is small, and the mobility of moving state is small, the constant acceleration…
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The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks

Ohio State Univ-Mustafa Ridvan Cantas, Karina Meneses Cime
Ohio State University-Ozgenur Kavas-Torris, Bilin Aksun Guvenc, Levent Guvenc
  • Technical Paper
  • 2020-01-0137
To be published on 2020-04-14 by SAE International in United States
With the current drive of automotive and technology companies towards producing vehicles with higher levels of autonomy, it is inevitable that there will be an increasing number of SAE level L4-L5 autonomous vehicles (AVs) on roadways in the near future. The effect of this gradually increasing penetration of AVs on mobility, viewed as traffic congestion or traffic flow efficiency in this paper, and fuel efficiency improvement for the individual AV and for the whole road network with a mixed traffic of AVs and non-AVs is currently not well known. Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the possible effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both autonomous and non-autonomous vehicles. In this paper, L4-L5 AVs with varying penetration rates in total traffic flow were simulated using the microscopic traffic simulator Vissim on urban, mixed and freeway roadways to study the effect of penetration rate on fuel consumption and efficiency of traffic flow. The roadways used in…